Embodiments of the present invention relate to a method and a device for determining a contour and a center of an object. Further, embodiments relate to a segmenting of leukocytes in microscopic images of blood smears.
One important part of hematology is the differential blood count. Systems from the field of “computer-assisted microscopy” (CAM) enable an automatic analysis of blood smears and support the hematologist in the classification of the cells and thus form a supplement for modern hematological laboratory diagnostics.
Modern hematology systems provide important information on the cell population of peripheral blood in a fast, precise and highly efficient way. However, frequently in hospitals and laboratories up to 40% of the samples are post-differentiated manually under the microscope. In particular this last step may be accelerated and objectified by means of a system of computer-assisted microscopy. Here, both the working effort is reduced and also the quality of diagnosis is increased, which applies in particular also to abnormal blood samples.
Based on innovative concepts of image processing, leukocytes (white blood cells) are localized in blood smears and classified into clinically relevant sub-classes. Here, reference data sets pre-classified by experts, which may be expanded at any time, are frequently taken as the basis for classification.
The reliable detection and exact segmentation of white blood cells in colored blood smears of peripheral blood forms the basis for the automatic image-based generation of the differential blood count in the context of medical laboratory diagnostics. Frequently, in a first step a segmentation is executed with low resolution (e.g. 10× lens) for localizing the white blood cells. In a second step with a higher resolution (e.g. with a 100× lens), the exact segmentation of the cells is a precondition for the high accuracy of the following steps of classification. The accuracy of segmentation also relates, for example, to an exact segmentation of the nucleus and plasma of the cell, which is in particular desirable to be able to separately determine the features for the nucleus and the plasma. The accuracy of segmentation finally has a decisive effect on the quality of the classification of the cell.
The variety of white blood cells occurring in a blood smear in connection with their respective characteristic color distribution and texturing increase the difficulty of classification in complete automation. With conventional methods for the segmentation of white blood cells the exact separation of the nucleus and the plasma and the separation of neighboring erythrocytes (red blood cells) is not solved satisfactorily. Likewise, the known algorithms do not treat all types of leukocytes.
Examples of conventional methods for segmentation are described in the following documents: SINHA, N. and A. RAMAKRISHNAN: Automation of differential blood count. In: TENCON 2003. Conf. on convergent Technologies for Asia-Pacific Ragion, Vol. 2, p. 547-551, 2003; LIAO, Q. and Y. DENG: An accurate segmentation method for white blood cell images. In: IEEE Intern. Symposium on Biomedical Imaging, p. 245-248, 2002; RAMOSER H., V. LAURAIN, H. BISCHOF and R. ECKER: Leukocyte segmentation and classification in blood-smear images. In: 27th Annual Intern. Conf. of the Engineering in Medicine and Biology Society, S. 3,371-3,374, September 2005; LEZORAY, O and H. Cardot: Cooperation of color pixel classification schemes and color watershed: a study for microscopic images. IEEE Trans. on Image Processing, 11(7): 783-789, 2002; NILSSON, B. and A. HEYDEN: Segmentation of Dense Leukocyte clusters. P. 221-229, 2001; ONGUN, G., U. HALICI and K. LEBLEBICIOGLU: Automated contour detection in blood cell images by an efficient snake algorithm. Nonlinear Analysis, 47:5839-5847(9), 2001.
For the identification of regions in a characteristic color or color distribution, threshold value methods are frequently used. Conventional threshold value methods include, for example, the method of Otsu (OTSU, N.: A threshold selection method from gray level histograms. IEEE Trans. Systems, Man and Cybernetics, 9:62-66, 1979) the method of Kittler (KITTLER, J. and J. ILLINGWORTH: On threshold selection using clustering criteria. IEEE Trans. Systems, Man and Cybernetics, 15(5): 652-654, 1985; KITTLER, J. and J. ILLINGWORTH: Minimum error thresholding. 19(1): 41-47, 1986) or the method of Kapur (KAPUR, J., P. SAHOO and A. WONG: A new method for gray level picture thresholding using the entropy of histogram. Computer Vision, Graphics and Image Processing, 29(3): 273-285, 1985)
To finally obtain a continuous contour of the object or the cell, for example path-tracking algorithms may be used. The method of Dijkstra (DIJKSTRA, E. W.: A note on two problems in connexion with graphs. In: Numerische Mathematik, Vol. 1, p. 269-271. Mathematisch Centrum, Amsterdam, The Netherlands, 1959) represents a possible conventional path-tracking method.
Based on this conventional technology, it is the object to provide a method for segmentation (e.g. of leukocytes in differential blood counts) which—in contrast to conventional methods—solves the following issues:    (a) robust segmentation of different cell classes,    (b) robust segmentation with neighboring erythrocytes and    (c) robust segmentation with variations in coloring and illumination.
Apart from that it is desirable not only to detect and segment blood cells but generally to reliably detect contours of objects in digital images to be able to subsequently classify the same.